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Showing content from https://python.langchain.com/docs/integrations/document_loaders/google_firestore/ below:

Google Firestore (Native Mode) | 🦜️🔗 LangChain

Firestore is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's Langchain integrations.

This notebook goes over how to use Firestore to save, load and delete langchain documents with FirestoreLoader and FirestoreSaver.

Learn more about the package on GitHub.

To run this notebook, you will need to do the following:

• Create a Google Cloud Project
• Enable the Firestore API
• Create a Firestore database

After confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts.

The integration lives in its own langchain-google-firestore package, so we need to install it.

%pip install --upgrade --quiet langchain-google-firestore

Colab only: Uncomment the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top.

Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.

If you don't know your project ID, try the following:

• Run gcloud config list.
• Run gcloud projects list.
• See the support page: Locate the project ID.

PROJECT_ID = "my-project-id"  


!gcloud config set project {PROJECT_ID}

Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.

• If you are using Colab to run this notebook, use the cell below and continue.
• If you are using Vertex AI Workbench, check out the setup instructions here.

from google.colab import auth

auth.authenticate_user()

FirestoreSaver can store Documents into Firestore. By default it will try to extract the Document reference from the metadata

Save langchain documents with FirestoreSaver.upsert_documents(<documents>).

from langchain_core.documents import Document
from langchain_google_firestore import FirestoreSaver

saver = FirestoreSaver()

data = [Document(page_content="Hello, World!")]

saver.upsert_documents(data)

If a collection is specified the documents will be stored with an auto generated id.

saver = FirestoreSaver("Collection")

saver.upsert_documents(data)

doc_ids = ["AnotherCollection/doc_id", "foo/bar"]
saver = FirestoreSaver()

saver.upsert_documents(documents=data, document_ids=doc_ids)

Load langchain documents with FirestoreLoader.load() or Firestore.lazy_load(). lazy_load returns a generator that only queries database during the iteration. To initialize FirestoreLoader class you need to provide:

1. source - An instance of a Query, CollectionGroup, DocumentReference or the single \-delimited path to a Firestore collection.

from langchain_google_firestore import FirestoreLoader

loader_collection = FirestoreLoader("Collection")
loader_subcollection = FirestoreLoader("Collection/doc/SubCollection")


data_collection = loader_collection.load()
data_subcollection = loader_subcollection.load()

from google.cloud import firestore

client = firestore.Client()
doc_ref = client.collection("foo").document("bar")

loader_document = FirestoreLoader(doc_ref)

data = loader_document.load()

from google.cloud.firestore import CollectionGroup, FieldFilter, Query

col_ref = client.collection("col_group")
collection_group = CollectionGroup(col_ref)

loader_group = FirestoreLoader(collection_group)

col_ref = client.collection("collection")
query = col_ref.where(filter=FieldFilter("region", "==", "west_coast"))

loader_query = FirestoreLoader(query)

Delete a list of langchain documents from Firestore collection with FirestoreSaver.delete_documents(<documents>).

If document ids is provided, the Documents will be ignored.

saver = FirestoreSaver()

saver.delete_documents(data)


saver.delete_documents(data, doc_ids)

The arguments of page_content_fields and metadata_fields will specify the Firestore Document fields to be written into LangChain Document page_content and metadata.

loader = FirestoreLoader(
    source="foo/bar/subcol",
    page_content_fields=["data_field"],
    metadata_fields=["metadata_field"],
)

data = loader.load()

When the page_content contains only one field the information will be the field value only. Otherwise the page_content will be in JSON format.

from google.auth import compute_engine
from google.cloud.firestore import Client

client = Client(database="non-default-db", creds=compute_engine.Credentials())
loader = FirestoreLoader(
    source="foo",
    client=client,
)

• Document loader conceptual guide
• Document loader how-to guides

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